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library(tidyverse)
library(dplyr)
library(tidyverse)
library(caret)
library(rpart)
library(rpart.plot)
library(kernlab)
library(ggplot2)
library(tidyverse)
library(maps)
library(mapproj)
library(jsonlite)
library(corrr)

HousePrices <- read_csv('house_prices.csv')

Warning: One or more parsing issues, call `problems()` on your data
frame for details, e.g.:
  dat <- vroom(...)
  problems(dat)Rows: 187531 Columns: 21── Column specification ───────────────────────────────────────
Delimiter: ","
chr (15): Title, Description, Amount(in rupees), location, ...
dbl  (4): Index, Price (in rupees), Bathroom, Balcony
lgl  (2): Dimensions, Plot Area
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

colnames(HousePrices)

 [1] "Index"             "Title"            
 [3] "Description"       "Amount(in rupees)"
 [5] "Price (in rupees)" "location"         
 [7] "Carpet Area"       "Status"           
 [9] "Floor"             "Transaction"      
[11] "Furnishing"        "facing"           
[13] "overlooking"       "Society"          
[15] "Bathroom"          "Balcony"          
[17] "Car Parking"       "Ownership"        
[19] "Super Area"        "Dimensions"       
[21] "Plot Area"        

colnames(HousePrices)

 [1] "Index"             "Title"            
 [3] "Description"       "Amount(in rupees)"
 [5] "Price (in rupees)" "location"         
 [7] "Carpet Area"       "Status"           
 [9] "Floor"             "Transaction"      
[11] "Furnishing"        "facing"           
[13] "overlooking"       "Society"          
[15] "Bathroom"          "Balcony"          
[17] "Car Parking"       "Ownership"        
[19] "Super Area"        "Dimensions"       
[21] "Plot Area"        

row.names(HousePrices)

   [1] "1"    "2"    "3"    "4"    "5"    "6"    "7"    "8"   
   [9] "9"    "10"   "11"   "12"   "13"   "14"   "15"   "16"  
  [17] "17"   "18"   "19"   "20"   "21"   "22"   "23"   "24"  
  [25] "25"   "26"   "27"   "28"   "29"   "30"   "31"   "32"  
  [33] "33"   "34"   "35"   "36"   "37"   "38"   "39"   "40"  
  [41] "41"   "42"   "43"   "44"   "45"   "46"   "47"   "48"  
  [49] "49"   "50"   "51"   "52"   "53"   "54"   "55"   "56"  
  [57] "57"   "58"   "59"   "60"   "61"   "62"   "63"   "64"  
  [65] "65"   "66"   "67"   "68"   "69"   "70"   "71"   "72"  
  [73] "73"   "74"   "75"   "76"   "77"   "78"   "79"   "80"  
  [81] "81"   "82"   "83"   "84"   "85"   "86"   "87"   "88"  
  [89] "89"   "90"   "91"   "92"   "93"   "94"   "95"   "96"  
  [97] "97"   "98"   "99"   "100"  "101"  "102"  "103"  "104" 
 [105] "105"  "106"  "107"  "108"  "109"  "110"  "111"  "112" 
 [113] "113"  "114"  "115"  "116"  "117"  "118"  "119"  "120" 
 [121] "121"  "122"  "123"  "124"  "125"  "126"  "127"  "128" 
 [129] "129"  "130"  "131"  "132"  "133"  "134"  "135"  "136" 
 [137] "137"  "138"  "139"  "140"  "141"  "142"  "143"  "144" 
 [145] "145"  "146"  "147"  "148"  "149"  "150"  "151"  "152" 
 [153] "153"  "154"  "155"  "156"  "157"  "158"  "159"  "160" 
 [161] "161"  "162"  "163"  "164"  "165"  "166"  "167"  "168" 
 [169] "169"  "170"  "171"  "172"  "173"  "174"  "175"  "176" 
 [177] "177"  "178"  "179"  "180"  "181"  "182"  "183"  "184" 
 [185] "185"  "186"  "187"  "188"  "189"  "190"  "191"  "192" 
 [193] "193"  "194"  "195"  "196"  "197"  "198"  "199"  "200" 
 [201] "201"  "202"  "203"  "204"  "205"  "206"  "207"  "208" 
 [209] "209"  "210"  "211"  "212"  "213"  "214"  "215"  "216" 
 [217] "217"  "218"  "219"  "220"  "221"  "222"  "223"  "224" 
 [225] "225"  "226"  "227"  "228"  "229"  "230"  "231"  "232" 
 [233] "233"  "234"  "235"  "236"  "237"  "238"  "239"  "240" 
 [241] "241"  "242"  "243"  "244"  "245"  "246"  "247"  "248" 
 [249] "249"  "250"  "251"  "252"  "253"  "254"  "255"  "256" 
 [257] "257"  "258"  "259"  "260"  "261"  "262"  "263"  "264" 
 [265] "265"  "266"  "267"  "268"  "269"  "270"  "271"  "272" 
 [273] "273"  "274"  "275"  "276"  "277"  "278"  "279"  "280" 
 [281] "281"  "282"  "283"  "284"  "285"  "286"  "287"  "288" 
 [289] "289"  "290"  "291"  "292"  "293"  "294"  "295"  "296" 
 [297] "297"  "298"  "299"  "300"  "301"  "302"  "303"  "304" 
 [305] "305"  "306"  "307"  "308"  "309"  "310"  "311"  "312" 
 [313] "313"  "314"  "315"  "316"  "317"  "318"  "319"  "320" 
 [321] "321"  "322"  "323"  "324"  "325"  "326"  "327"  "328" 
 [329] "329"  "330"  "331"  "332"  "333"  "334"  "335"  "336" 
 [337] "337"  "338"  "339"  "340"  "341"  "342"  "343"  "344" 
 [345] "345"  "346"  "347"  "348"  "349"  "350"  "351"  "352" 
 [353] "353"  "354"  "355"  "356"  "357"  "358"  "359"  "360" 
 [361] "361"  "362"  "363"  "364"  "365"  "366"  "367"  "368" 
 [369] "369"  "370"  "371"  "372"  "373"  "374"  "375"  "376" 
 [377] "377"  "378"  "379"  "380"  "381"  "382"  "383"  "384" 
 [385] "385"  "386"  "387"  "388"  "389"  "390"  "391"  "392" 
 [393] "393"  "394"  "395"  "396"  "397"  "398"  "399"  "400" 
 [401] "401"  "402"  "403"  "404"  "405"  "406"  "407"  "408" 
 [409] "409"  "410"  "411"  "412"  "413"  "414"  "415"  "416" 
 [417] "417"  "418"  "419"  "420"  "421"  "422"  "423"  "424" 
 [425] "425"  "426"  "427"  "428"  "429"  "430"  "431"  "432" 
 [433] "433"  "434"  "435"  "436"  "437"  "438"  "439"  "440" 
 [441] "441"  "442"  "443"  "444"  "445"  "446"  "447"  "448" 
 [449] "449"  "450"  "451"  "452"  "453"  "454"  "455"  "456" 
 [457] "457"  "458"  "459"  "460"  "461"  "462"  "463"  "464" 
 [465] "465"  "466"  "467"  "468"  "469"  "470"  "471"  "472" 
 [473] "473"  "474"  "475"  "476"  "477"  "478"  "479"  "480" 
 [481] "481"  "482"  "483"  "484"  "485"  "486"  "487"  "488" 
 [489] "489"  "490"  "491"  "492"  "493"  "494"  "495"  "496" 
 [497] "497"  "498"  "499"  "500"  "501"  "502"  "503"  "504" 
 [505] "505"  "506"  "507"  "508"  "509"  "510"  "511"  "512" 
 [513] "513"  "514"  "515"  "516"  "517"  "518"  "519"  "520" 
 [521] "521"  "522"  "523"  "524"  "525"  "526"  "527"  "528" 
 [529] "529"  "530"  "531"  "532"  "533"  "534"  "535"  "536" 
 [537] "537"  "538"  "539"  "540"  "541"  "542"  "543"  "544" 
 [545] "545"  "546"  "547"  "548"  "549"  "550"  "551"  "552" 
 [553] "553"  "554"  "555"  "556"  "557"  "558"  "559"  "560" 
 [561] "561"  "562"  "563"  "564"  "565"  "566"  "567"  "568" 
 [569] "569"  "570"  "571"  "572"  "573"  "574"  "575"  "576" 
 [577] "577"  "578"  "579"  "580"  "581"  "582"  "583"  "584" 
 [585] "585"  "586"  "587"  "588"  "589"  "590"  "591"  "592" 
 [593] "593"  "594"  "595"  "596"  "597"  "598"  "599"  "600" 
 [601] "601"  "602"  "603"  "604"  "605"  "606"  "607"  "608" 
 [609] "609"  "610"  "611"  "612"  "613"  "614"  "615"  "616" 
 [617] "617"  "618"  "619"  "620"  "621"  "622"  "623"  "624" 
 [625] "625"  "626"  "627"  "628"  "629"  "630"  "631"  "632" 
 [633] "633"  "634"  "635"  "636"  "637"  "638"  "639"  "640" 
 [641] "641"  "642"  "643"  "644"  "645"  "646"  "647"  "648" 
 [649] "649"  "650"  "651"  "652"  "653"  "654"  "655"  "656" 
 [657] "657"  "658"  "659"  "660"  "661"  "662"  "663"  "664" 
 [665] "665"  "666"  "667"  "668"  "669"  "670"  "671"  "672" 
 [673] "673"  "674"  "675"  "676"  "677"  "678"  "679"  "680" 
 [681] "681"  "682"  "683"  "684"  "685"  "686"  "687"  "688" 
 [689] "689"  "690"  "691"  "692"  "693"  "694"  "695"  "696" 
 [697] "697"  "698"  "699"  "700"  "701"  "702"  "703"  "704" 
 [705] "705"  "706"  "707"  "708"  "709"  "710"  "711"  "712" 
 [713] "713"  "714"  "715"  "716"  "717"  "718"  "719"  "720" 
 [721] "721"  "722"  "723"  "724"  "725"  "726"  "727"  "728" 
 [729] "729"  "730"  "731"  "732"  "733"  "734"  "735"  "736" 
 [737] "737"  "738"  "739"  "740"  "741"  "742"  "743"  "744" 
 [745] "745"  "746"  "747"  "748"  "749"  "750"  "751"  "752" 
 [753] "753"  "754"  "755"  "756"  "757"  "758"  "759"  "760" 
 [761] "761"  "762"  "763"  "764"  "765"  "766"  "767"  "768" 
 [769] "769"  "770"  "771"  "772"  "773"  "774"  "775"  "776" 
 [777] "777"  "778"  "779"  "780"  "781"  "782"  "783"  "784" 
 [785] "785"  "786"  "787"  "788"  "789"  "790"  "791"  "792" 
 [793] "793"  "794"  "795"  "796"  "797"  "798"  "799"  "800" 
 [801] "801"  "802"  "803"  "804"  "805"  "806"  "807"  "808" 
 [809] "809"  "810"  "811"  "812"  "813"  "814"  "815"  "816" 
 [817] "817"  "818"  "819"  "820"  "821"  "822"  "823"  "824" 
 [825] "825"  "826"  "827"  "828"  "829"  "830"  "831"  "832" 
 [833] "833"  "834"  "835"  "836"  "837"  "838"  "839"  "840" 
 [841] "841"  "842"  "843"  "844"  "845"  "846"  "847"  "848" 
 [849] "849"  "850"  "851"  "852"  "853"  "854"  "855"  "856" 
 [857] "857"  "858"  "859"  "860"  "861"  "862"  "863"  "864" 
 [865] "865"  "866"  "867"  "868"  "869"  "870"  "871"  "872" 
 [873] "873"  "874"  "875"  "876"  "877"  "878"  "879"  "880" 
 [881] "881"  "882"  "883"  "884"  "885"  "886"  "887"  "888" 
 [889] "889"  "890"  "891"  "892"  "893"  "894"  "895"  "896" 
 [897] "897"  "898"  "899"  "900"  "901"  "902"  "903"  "904" 
 [905] "905"  "906"  "907"  "908"  "909"  "910"  "911"  "912" 
 [913] "913"  "914"  "915"  "916"  "917"  "918"  "919"  "920" 
 [921] "921"  "922"  "923"  "924"  "925"  "926"  "927"  "928" 
 [929] "929"  "930"  "931"  "932"  "933"  "934"  "935"  "936" 
 [937] "937"  "938"  "939"  "940"  "941"  "942"  "943"  "944" 
 [945] "945"  "946"  "947"  "948"  "949"  "950"  "951"  "952" 
 [953] "953"  "954"  "955"  "956"  "957"  "958"  "959"  "960" 
 [961] "961"  "962"  "963"  "964"  "965"  "966"  "967"  "968" 
 [969] "969"  "970"  "971"  "972"  "973"  "974"  "975"  "976" 
 [977] "977"  "978"  "979"  "980"  "981"  "982"  "983"  "984" 
 [985] "985"  "986"  "987"  "988"  "989"  "990"  "991"  "992" 
 [993] "993"  "994"  "995"  "996"  "997"  "998"  "999"  "1000"
 [ reached getOption("max.print") -- omitted 186531 entries ]

head(HousePrices)

getwd()

[1] "/Users/shaunmckellarjr/Desktop/Intro to Data Science Class/Intro to DS project FIles"

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str(HousePrices)

spc_tbl_ [187,531 × 21] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Index            : num [1:187531] 0 1 2 3 4 5 6 7 8 9 ...
 $ Title            : chr [1:187531] "1 BHK Ready to Occupy Flat for sale in Srushti Siddhi Mangal Murti Complex Bhiwandi" "2 BHK Ready to Occupy Flat for sale in Dosti Vihar Pokhran Road" "2 BHK Ready to Occupy Flat for sale in Sunrise by Kalpataru Kolshet Road" "1 BHK Ready to Occupy Flat for sale Kasheli" ...
 $ Description      : chr [1:187531] "Bhiwandi, Thane has an attractive 1 BHK Flat for sale. The property is ideally located in a strategic location "| __truncated__ "One can find this stunning 2 BHK flat for sale in Pokhran Road, Thane. It enjoys an excellent location within t"| __truncated__ "Up for immediate sale is a 2 BHK apartment in Kolshet Road, Thane. Don't miss this bargain flat for sale. Situa"| __truncated__ "This beautiful 1 BHK Flat is available for sale in Kasheli, Thane. This flat for resale has a desirable locatio"| __truncated__ ...
 $ Amount(in rupees): chr [1:187531] "42 Lac" "98 Lac" "1.40 Cr" "25 Lac" ...
 $ Price (in rupees): num [1:187531] 6000 13799 17500 NA 18824 ...
 $ location         : chr [1:187531] "thane" "thane" "thane" "thane" ...
 $ Carpet Area      : chr [1:187531] "500 sqft" "473 sqft" "779 sqft" "530 sqft" ...
 $ Status           : chr [1:187531] "Ready to Move" "Ready to Move" "Ready to Move" "Ready to Move" ...
 $ Floor            : chr [1:187531] "10 out of 11" "3 out of 22" "10 out of 29" "1 out of 3" ...
 $ Transaction      : chr [1:187531] "Resale" "Resale" "Resale" "Resale" ...
 $ Furnishing       : chr [1:187531] "Unfurnished" "Semi-Furnished" "Unfurnished" "Unfurnished" ...
 $ facing           : chr [1:187531] NA "East" "East" NA ...
 $ overlooking      : chr [1:187531] NA "Garden/Park" "Garden/Park" NA ...
 $ Society          : chr [1:187531] "Srushti Siddhi Mangal Murti Complex" "Dosti Vihar" "Sunrise by Kalpataru" NA ...
 $ Bathroom         : num [1:187531] 1 2 2 1 2 1 1 1 1 3 ...
 $ Balcony          : num [1:187531] 2 NA NA 1 NA 1 NA NA NA 1 ...
 $ Car Parking      : chr [1:187531] NA "1 Open" "1 Covered" NA ...
 $ Ownership        : chr [1:187531] NA "Freehold" "Freehold" NA ...
 $ Super Area       : chr [1:187531] NA NA NA NA ...
 $ Dimensions       : logi [1:187531] NA NA NA NA NA NA ...
 $ Plot Area        : logi [1:187531] NA NA NA NA NA NA ...
 - attr(*, "spec")=
  .. cols(
  ..   Index = col_double(),
  ..   Title = col_character(),
  ..   Description = col_character(),
  ..   `Amount(in rupees)` = col_character(),
  ..   `Price (in rupees)` = col_double(),
  ..   location = col_character(),
  ..   `Carpet Area` = col_character(),
  ..   Status = col_character(),
  ..   Floor = col_character(),
  ..   Transaction = col_character(),
  ..   Furnishing = col_character(),
  ..   facing = col_character(),
  ..   overlooking = col_character(),
  ..   Society = col_character(),
  ..   Bathroom = col_double(),
  ..   Balcony = col_double(),
  ..   `Car Parking` = col_character(),
  ..   Ownership = col_character(),
  ..   `Super Area` = col_character(),
  ..   Dimensions = col_logical(),
  ..   `Plot Area` = col_logical()
  .. )
 - attr(*, "problems")=<externalptr> 

missing_values <- colSums(is.na(HousePrices))

print(missing_values)

            Index             Title       Description Amount(in rupees) Price (in rupees) 
                0                 0              3023                 0             17665 
         location       Carpet Area            Status             Floor       Transaction 
                0             80673               615              7077                83 
       Furnishing            facing       overlooking           Society          Bathroom 
             2897             70233             81436            109678               863 
          Balcony       Car Parking         Ownership        Super Area        Dimensions 
            48957            103357             65517            107685            187531 
        Plot Area 
           187531 

str(HousePrices)

spc_tbl_ [187,531 × 21] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Index            : num [1:187531] 0 1 2 3 4 5 6 7 8 9 ...
 $ Title            : chr [1:187531] "1 BHK Ready to Occupy Flat for sale in Srushti Siddhi Mangal Murti Complex Bhiwandi" "2 BHK Ready to Occupy Flat for sale in Dosti Vihar Pokhran Road" "2 BHK Ready to Occupy Flat for sale in Sunrise by Kalpataru Kolshet Road" "1 BHK Ready to Occupy Flat for sale Kasheli" ...
 $ Description      : chr [1:187531] "Bhiwandi, Thane has an attractive 1 BHK Flat for sale. The property is ideally located in a strategic location "| __truncated__ "One can find this stunning 2 BHK flat for sale in Pokhran Road, Thane. It enjoys an excellent location within t"| __truncated__ "Up for immediate sale is a 2 BHK apartment in Kolshet Road, Thane. Don't miss this bargain flat for sale. Situa"| __truncated__ "This beautiful 1 BHK Flat is available for sale in Kasheli, Thane. This flat for resale has a desirable locatio"| __truncated__ ...
 $ Amount(in rupees): chr [1:187531] "42 Lac" "98 Lac" "1.40 Cr" "25 Lac" ...
 $ Price (in rupees): num [1:187531] 6000 13799 17500 NA 18824 ...
 $ location         : chr [1:187531] "thane" "thane" "thane" "thane" ...
 $ Carpet Area      : chr [1:187531] "500 sqft" "473 sqft" "779 sqft" "530 sqft" ...
 $ Status           : chr [1:187531] "Ready to Move" "Ready to Move" "Ready to Move" "Ready to Move" ...
 $ Floor            : chr [1:187531] "10 out of 11" "3 out of 22" "10 out of 29" "1 out of 3" ...
 $ Transaction      : chr [1:187531] "Resale" "Resale" "Resale" "Resale" ...
 $ Furnishing       : chr [1:187531] "Unfurnished" "Semi-Furnished" "Unfurnished" "Unfurnished" ...
 $ facing           : chr [1:187531] NA "East" "East" NA ...
 $ overlooking      : chr [1:187531] NA "Garden/Park" "Garden/Park" NA ...
 $ Society          : chr [1:187531] "Srushti Siddhi Mangal Murti Complex" "Dosti Vihar" "Sunrise by Kalpataru" NA ...
 $ Bathroom         : num [1:187531] 1 2 2 1 2 1 1 1 1 3 ...
 $ Balcony          : num [1:187531] 2 NA NA 1 NA 1 NA NA NA 1 ...
 $ Car Parking      : chr [1:187531] NA "1 Open" "1 Covered" NA ...
 $ Ownership        : chr [1:187531] NA "Freehold" "Freehold" NA ...
 $ Super Area       : chr [1:187531] NA NA NA NA ...
 $ Dimensions       : logi [1:187531] NA NA NA NA NA NA ...
 $ Plot Area        : logi [1:187531] NA NA NA NA NA NA ...
 - attr(*, "spec")=
  .. cols(
  ..   Index = col_double(),
  ..   Title = col_character(),
  ..   Description = col_character(),
  ..   `Amount(in rupees)` = col_character(),
  ..   `Price (in rupees)` = col_double(),
  ..   location = col_character(),
  ..   `Carpet Area` = col_character(),
  ..   Status = col_character(),
  ..   Floor = col_character(),
  ..   Transaction = col_character(),
  ..   Furnishing = col_character(),
  ..   facing = col_character(),
  ..   overlooking = col_character(),
  ..   Society = col_character(),
  ..   Bathroom = col_double(),
  ..   Balcony = col_double(),
  ..   `Car Parking` = col_character(),
  ..   Ownership = col_character(),
  ..   `Super Area` = col_character(),
  ..   Dimensions = col_logical(),
  ..   `Plot Area` = col_logical()
  .. )
 - attr(*, "problems")=<externalptr> 

ncol(HousePrices)

[1] 21

nrow(HousePrices)

[1] 187531

str(missing_values)

 Named num [1:21] 0 0 3023 0 17665 ...
 - attr(*, "names")= chr [1:21] "Index" "Title" "Description" "Amount(in rupees)" ...

threshold_percent <- 50

# Calculate the percentage of missing values in each column
missing_percent <- (colSums(is.na(HousePrices)) / nrow(HousePrices)) * 100

columns_to_keep <- missing_percent <= threshold_percent

filtered_Housing_Prices <- HousePrices[, columns_to_keep]

str(filtered_Housing_Prices)

tibble [187,531 × 16] (S3: tbl_df/tbl/data.frame)
 $ Index            : num [1:187531] 0 1 2 3 4 5 6 7 8 9 ...
 $ Title            : chr [1:187531] "1 BHK Ready to Occupy Flat for sale in Srushti Siddhi Mangal Murti Complex Bhiwandi" "2 BHK Ready to Occupy Flat for sale in Dosti Vihar Pokhran Road" "2 BHK Ready to Occupy Flat for sale in Sunrise by Kalpataru Kolshet Road" "1 BHK Ready to Occupy Flat for sale Kasheli" ...
 $ Description      : chr [1:187531] "Bhiwandi, Thane has an attractive 1 BHK Flat for sale. The property is ideally located in a strategic location "| __truncated__ "One can find this stunning 2 BHK flat for sale in Pokhran Road, Thane. It enjoys an excellent location within t"| __truncated__ "Up for immediate sale is a 2 BHK apartment in Kolshet Road, Thane. Don't miss this bargain flat for sale. Situa"| __truncated__ "This beautiful 1 BHK Flat is available for sale in Kasheli, Thane. This flat for resale has a desirable locatio"| __truncated__ ...
 $ Amount(in rupees): chr [1:187531] "42 Lac" "98 Lac" "1.40 Cr" "25 Lac" ...
 $ Price (in rupees): num [1:187531] 6000 13799 17500 NA 18824 ...
 $ location         : chr [1:187531] "thane" "thane" "thane" "thane" ...
 $ Carpet Area      : chr [1:187531] "500 sqft" "473 sqft" "779 sqft" "530 sqft" ...
 $ Status           : chr [1:187531] "Ready to Move" "Ready to Move" "Ready to Move" "Ready to Move" ...
 $ Floor            : chr [1:187531] "10 out of 11" "3 out of 22" "10 out of 29" "1 out of 3" ...
 $ Transaction      : chr [1:187531] "Resale" "Resale" "Resale" "Resale" ...
 $ Furnishing       : chr [1:187531] "Unfurnished" "Semi-Furnished" "Unfurnished" "Unfurnished" ...
 $ facing           : chr [1:187531] NA "East" "East" NA ...
 $ overlooking      : chr [1:187531] NA "Garden/Park" "Garden/Park" NA ...
 $ Bathroom         : num [1:187531] 1 2 2 1 2 1 1 1 1 3 ...
 $ Balcony          : num [1:187531] 2 NA NA 1 NA 1 NA NA NA 1 ...
 $ Ownership        : chr [1:187531] NA "Freehold" "Freehold" NA ...

print(filtered_Housing_Prices)

filtered_Housing_Prices

# Drop rows with NaN values in the 'Price (in rupees)' column
filtered_housing_prices <- HousePrices[complete.cases(HousePrices$`Price (in rupees)`),]

filtered_housing_prices

# Drop rows with NaN values in the 'Carpet Area' column
filtered_house_prices <- HousePrices[complete.cases(HousePrices$`Carpet Area`), ]

print(filtered_house_prices)

# Remove 'Dimensions' and 'Plot Area' columns
filtered_house_prices <- HousePrices[, !names(HousePrices) %in% c("Dimensions", "Plot Area")]

print(filtered_Housing_Prices)

# Convert 'Price (in rupees)' values from Lakhs to Crores
filtered_house_prices$`Price (in rupees)` <- filtered_house_prices$`Price (in rupees)` / 100

# Display the 'Price (in rupees)' column
head(filtered_house_prices$`Price (in rupees)`)

[1]  60.00 137.99 175.00     NA 188.24  66.18

head(filtered_Housing_Prices)

# Remove rows with NA values
Cleaned_House_Prices <- na.omit(filtered_Housing_Prices)

head(Cleaned_House_Prices)

str(Cleaned_House_Prices)

tibble [64,430 × 16] (S3: tbl_df/tbl/data.frame)
 $ Index            : num [1:64430] 9 13 15 16 21 28 29 30 31 32 ...
 $ Title            : chr [1:64430] "3 BHK Ready to Occupy Flat for sale in Pride Palms Kolshet" "4 BHK Ready to Occupy Flat for sale Hiranandani Estate" "2 BHK Ready to Occupy Flat for sale in Puraniks Tokyo Bay Kasarvadavali" "1 BHK Ready to Occupy Flat for sale in Green Woods Shilphata" ...
 $ Description      : chr [1:64430] "One can find this stunning 3 BHK flat for sale in Kolshet, Thane. Ideally situated in the Pride Palms township "| __truncated__ "Creatively planned and constructed is a 4 BHK flat for sale in Hiranandani Estate, Thane. This flat is an attra"| __truncated__ "Kasarvadavali, Thane has an appealing 2 BHK flat for sale with various amenities. It is housed in the well-plan"| __truncated__ "Have a look at this immaculate 1 BHK flat for sale in Shilphata, Thane. Situated in the Green Woods township, i"| __truncated__ ...
 $ Amount(in rupees): chr [1:64430] "1.60 Cr" "4.25 Cr" "90 Lac" "37 Lac" ...
 $ Price (in rupees): num [1:64430] 11150 17526 10000 5736 15000 ...
 $ location         : chr [1:64430] "thane" "thane" "thane" "thane" ...
 $ Carpet Area      : chr [1:64430] "900 sqft" "1820 sqft" "675 sqft" "647 sqft" ...
 $ Status           : chr [1:64430] "Ready to Move" "Ready to Move" "Ready to Move" "Ready to Move" ...
 $ Floor            : chr [1:64430] "3 out of 27" "18 out of 27" "10 out of 16" "5 out of 7" ...
 $ Transaction      : chr [1:64430] "Resale" "Resale" "New Property" "New Property" ...
 $ Furnishing       : chr [1:64430] "Unfurnished" "Semi-Furnished" "Unfurnished" "Unfurnished" ...
 $ facing           : chr [1:64430] "East" "East" "North - East" "East" ...
 $ overlooking      : chr [1:64430] "Garden/Park" "Garden/Park" "Garden/Park, Main Road" "Garden/Park" ...
 $ Bathroom         : num [1:64430] 3 4 2 1 2 3 1 3 2 2 ...
 $ Balcony          : num [1:64430] 1 1 1 2 2 3 1 1 2 1 ...
 $ Ownership        : chr [1:64430] "Freehold" "Freehold" "Freehold" "Freehold" ...
 - attr(*, "na.action")= 'omit' Named int [1:123101] 1 2 3 4 5 6 7 8 9 11 ...
  ..- attr(*, "names")= chr [1:123101] "1" "2" "3" "4" ...

view(Cleaned_House_Prices$`Carpet Area`)

Showing the first 1000 rows.

# Create a bar plot to compare the counts of "Ready to Move" vs. "Not Ready"
ggplot(data = Cleaned_House_Prices, aes(x = Status, fill = Status)) +
  geom_bar() +
  labs(title = "Comparison of 'Ready to Move' vs. 'Not Ready'",
       x = "Status",
       y = "Count") +
  scale_fill_manual(values = c("Ready to Move" = "green", "Not Ready" = "red"))

# Print the correlation coefficient
print(correlation)

[1] -0.02206333          NA  0.09250467  0.04802626

view(Cleaned_House_Prices$Status)

Showing the first 1000 rows.